Differential radiological features of patients infected or colonised with slow-growing non-tuberculous mycobacteria.

Non-tuberculous mycobacterial pulmonary disease (NTM-PD) is considered a growing health concern. The majority of NTM-PD cases in Europe are caused by slow-growing mycobacteria (SGM). However, distinct radiological features of different SGM remain largely uninvestigated. We applied a previously described radiological score to a patient cohort consisting of individuals with isolation of different SGM. Correlations between clinical data, species and computed tomography (CT) features were examined by logistic and linear regression analyses, as well as over the course of time. Overall, 135 pulmonary CT scans from 84 patients were included. The isolated NTM-species were mainly Mycobacterium avium complex (MAC, n = 49), as well as 35 patients with non-MAC-species. Patients with isolation of M. intracellulare had more extensive CT findings compared to all other SGM species (coefficient 3.53, 95% Cl - 0.37 to 7.52, p = 0.075) while patients meeting the ATS criteria and not undergoing therapy exhibited an increase in CT scores over time. This study provides insights into differential radiological features of slow-growing NTM. While M. intracellulare exhibited a tendency towards higher overall CT scores, the radiological features were similar across different SGM. The applied CT score might be a useful instrument for monitoring patients and could help to guide antimycobacterial therapy.

Optimizing resource allocation: Cost-effectiveness of specified D-dimer cut-offs in cancer patients with suspected venous thromboembolism.

An accurate diagnosis of venous thromboembolism (VTE) is crucial, given the potential for high mortality in undetected cases. Strategic D-dimer testing may aid in identifying low-risk patients, preventing overdiagnosis and reducing imaging costs. We conducted a retrospective, comparative analysis to assess the potential cost savings that could be achieved by adopting different approaches to determine the most effective D-dimer cut-off value in cancer patients with suspected VTE, compared to the commonly used rule-out cut-off level of 0.5 mg/L. The study included 526 patients (median age 65, IQR 55-75) with a confirmed cancer diagnosis who underwent D-dimer testing. Among these patients, the VTE prevalence was 29% (n = 152). Each diagnostic strategy's sensitivity, specificity, negative likelihood ratio (NLR), as well as positive likelihood ratio (PLR), and the proportion of patients exhibiting a negative D-dimer test result, were calculated. The diagnostic strategy that demonstrated the best balance between specificity, sensitivity, NLR, and PLR, utilized an inverse age-specific cut-off level for D-dimer [0.5 + (66-age) × 0.01 mg/L]. This method yielded a PLR of 2.9 at a very low NLR for the exclusion of VTE. We observed a significant cost reduction of 4.6% and 1.0% for PE and DVT, respectively. The utilization of an age-adjusted cut-off [patient's age × 0.01 mg/L] resulted in the highest cost savings, reaching 8.1% for PE and 3.4% for DVT. Using specified D-dimer cut-offs in the diagnosis of VTE could improve economics, considering the limited occurrence of confirmed cases among patients with suspected VTE.

Genomic diversity and clinical relevance of Mycobacterium simiae.

Introduction: Mycobacterium simiae is a slow-growing non-tuberculous mycobacterium that can cause non-tuberculous mycobacterium (NTM) pulmonary disease and extrapulmonary infections. Until now, detailed genomic and clinical characteristics, as well as possible transmission routes of this rare pathogen remain largely unknown. Methods: We conducted whole genome sequencing of available M. simiae isolates collected at a tertiary care centre in Central Germany from 2006 to 2020 and set them into context with publicly available M. simiae complex sequences through phylogenetic analysis. Resistance, virulence and stress genes, as well as known Mycobacteriaceae plasmid sequences were detected in whole genome raw reads. Clinical data and course were retrieved and correlated with genomic data. Results: We included 33 M. simiae sensu stricto isolates from seven patients. M. simiae showed low clinical relevance with only two patients fulfilling American Thoracic Society (ATS) criteria in our cohort and three receiving NTM-effective therapy. The bacterial populations were highly stable over time periods of up to 14 years, and no instances of mixed or re-infections with other strains of M. simiae were observed. Clustering with <12 single nucleotide polymorphisms distance was evident among isolates from different patients; however, proof for human-to-human transmission could not be established from epidemiological data. Conclusion: Overall, the available sequence data for M. simiae complex was significantly extended and new insights into its pathogenomic traits were obtained. We demonstrate high longitudinal genomic stability within single patients. Although we cannot exclude human-to-human transmission, we consider it unlikely in the light of available epidemiological data.

From pixels to prognosis: Imaging biomarkers for discrimination and outcome prediction of pulmonary embolism : Original Research Article.

PURPOSE: Recent advancements in medical imaging have transformed diagnostic assessments, offering exciting possibilities for extracting biomarker-based information. This study aims to investigate the capabilities of a machine learning classifier that incorporates dual-energy computed tomography (DECT) radiomics. The primary focus is on discerning and predicting outcomes related to pulmonary embolism (PE). METHODS: The study included 131 participants who underwent pulmonary artery DECT angiography between January 2015 and March 2022. Among them, 104 patients received the final diagnosis of PE and 27 patients served as a control group. A total of 107 radiomic features were extracted for every case based on DECT imaging. The dataset was divided into training and test sets for model development and validation. Stepwise feature reduction identified the most relevant features, which were used to train a gradient-boosted tree model. Receiver operating characteristics analysis and Cox regression tests assessed the association of texture features with overall survival. RESULTS: The trained machine learning classifier achieved a classification accuracy of 0.94 for identifying patients with acute PE with an area under the receiver operating characteristic curve of 0.91. Radiomics features could be valuable for predicting outcomes in patients with PE, demonstrating strong prognostic capabilities in survival prediction (c-index, 0.991 [0.979-1.00], p = 0.0001) with a median follow-up of 130 days (IQR, 38-720). Notably, the inclusion of clinical or DECT parameters did not enhance predictive performance. CONCLUSION: In conclusion, our study underscores the promising potential of leveraging radiomics on DECT imaging for the identification of patients with acute PE and predicting their outcomes. This approach has the potential to improve clinical decision-making and patient management, offering efficiencies in time and resources by utilizing existing DECT imaging without the need for an additional scoring system.

Indicators for Hospitalization in Acute Pulmonary Embolism: Uncover the Association Between D-dimer Levels, Thrombus Volume and Radiomics.

BACKGROUND: The advent of advanced computed tomography (CT) technology and the field of radiomics has opened up new avenues in diagnostic assessments. Increasingly, there is substantial evidence advocating for the incorporation of quantitative imaging biomarkers in the clinical decision-making process. This study aimed to examine the correlation between D-dimer levels and thrombus size in acute pulmonary embolism (PE) combining dual-energy CT (DECT) and radiomics and to investigate the diagnostic utility of a machine learning classifier based on dual-energy computed tomography (DECT) radiomics for identifying patients with a complicated course, defined as at least hospitalization at IMC. METHODS: The study was conducted including 136 participants who underwent pulmonary artery CT angiography from January 2015 to March 2022. Based on DECT imaging, 107 radiomic features were extracted for each patient using standardized image processing. After dividing the dataset into training and test sets, stepwise feature reduction based on reproducibility, variable importance and correlation analyses were performed to select the most relevant features; these were used to train and validate the gradient-boosted tree models.Receiver operating characteristics (ROC) analysis was utilized to evaluate the association between volumetric, laboratory data and adverse outcomes. RESULTS: In the central PE group, we observed a significant correlation between thrombus volumetrics and D-dimer levels (p = 0.0037), as well as between thrombus volumetrics and hospitalization at the Intermediate Care Unit (IMC) (p = 0.0001). In contrast, no statistically significant differences were identified in thrombus sizes between patients who experienced complications and those who had a favorable course (p = 0.3162). The trained machine learning classifier achieved an accuracy of 61% and 55% in identifying patients with a complicated course, as indicated by an area under the ROC curve of 0.63 and 0.58. CONCLUSION: In conclusion, our findings indicate a positive correlation between D-dimer levels and central PE's pulmonary embolic burden. Thrombus volumetrics may serve as an indicator for complications and outcomes in acute PE patients. Thus, thrombus volumetrics, as opposed to D-dimers, could be an additional marker for evaluating embolic disease severity. Moreover, DECT-derived radiomic feature models show promise in identifying patients with a complicated course, such as hospitalization at IMC.

The Role of the Fatty Liver Index (FLI) in the Management of Non-Alcoholic Fatty Liver Disease: A Systematic Review.

Currently, non-alcoholic fatty liver disease is the most common liver disease worldwide, with a prevalence of 32%. It is much more common among men (40%) and among patients with metabolic comorbidities such as obesity, diabetes and dyslipidemia. Being an asymptomatic disease, the diagnosis is often established on the basis of imaging methods, with an important role given to abdominal ultrasonography, computed tomography and magnetic resonance imaging. In order to facilitate diagnosis, experts have introduced a series of blood biomarkers. Two biomarker panels are currently validated for the diagnosis of non-alcoholic fatty liver disease: the fatty liver index, and the hepatic steatosis index. The fatty liver index has been in use in medical practice for over 17 years and has demonstrated its accuracy in various studies that compared it with other diagnostic methods, highlighted its role in screening patients with cardiovascular risk and validated the effects of different diets and drugs that are proposed for the treatment of the disease. In the management of non-alcoholic fatty liver disease, the fatty liver index is an important algorithm in the diagnosis and prognosis of patients with metabolic risk. Taking into account the diversity of drugs to be approved in the treatment of non-alcoholic fatty liver disease, the fatty liver index will become an effective tool in monitoring the effects of these therapies.

Advanced biomedical imaging for accurate discrimination and prognostication of mediastinal masses.

BACKGROUND: To investigate the potential of radiomic features and dual-source dual-energy CT (DECT) parameters in differentiating between benign and malignant mediastinal masses and predicting patient outcomes. METHODS: In this retrospective study, we analysed data from 90 patients (38 females, mean age 51 ± 25 years) with confirmed mediastinal masses who underwent contrast-enhanced DECT. Attenuation, radiomic features and DECT-derived imaging parameters were evaluated by two experienced readers. We performed analysis of variance (ANOVA) and Chi-square statistic tests for data comparison. Receiver operating characteristic curve analysis and Cox regression tests were used to differentiate between mediastinal masses. RESULTS: Of the 90 mediastinal masses, 49 (54%) were benign, including cases of thymic hyperplasia/thymic rebound (n = 10), mediastinitis (n = 16) and thymoma (n = 23). The remaining 41 (46%) lesions were classified as malignant, consisting of lymphoma (n = 28), mediastinal tumour (n = 4) and thymic carcinoma (n = 9). Significant differences were observed between benign and malignant mediastinal masses in all DECT-derived parameters (p ≤ .001) and 38 radiomic features (p ≤ .044) obtained from contrast-enhanced DECT. The combination of these methods achieved an area under the curve of .98 (95% CI, .893-1.000; p < .001) to differentiate between benign and malignant masses, with 100% sensitivity and 91% specificity. Throughout a follow-up of 1800 days, a multiparametric model incorporating radiomic features, DECT parameters and gender showed promising prognostic power in predicting all-cause mortality (c-index = .8 [95% CI, .702-.890], p < .001). CONCLUSIONS: A multiparametric approach combining radiomic features and DECT-derived imaging biomarkers allows for accurate and noninvasive differentiation between benign and malignant masses in the anterior mediastinum.

Unveiling the diagnostic enigma of D-dimer testing in cancer patients: Current evidence and areas of application.

BACKGROUND: Cancer is a well-known risk factor for venous thromboembolism (VTE). A combined strategy of D-dimer testing and clinical pre-test probability is usually used to exclude VTE. However, its effectiveness is diminished in cancer patients due to reduced specificity, ultimately leading to a decreased clinical utility. This review article seeks to provide a comprehensive summary of how to interpret D-dimer testing in cancer patients. METHODS: In accordance with PRISMA standards, literature pertaining to the diagnostic and prognostic significance of D-dimer testing in cancer patients was carefully chosen from reputable sources such as PubMed and the Cochrane databases. RESULTS: D-dimers have not only a diagnostic value in ruling out VTE but can also serve as an aid for rule-in if their values exceed 10-times the upper limit of normal. This threshold allows a diagnosis of VTE in cancer patients with a positive predictive value of more than 80%. Moreover, elevated D-dimers carry important prognostic information and are associated with VTE reoccurrence. A gradual increase in risk for all-cause death suggests that VTE is also an indicator of biologically more aggressive cancer types and advanced cancer stages. Considering the lack of standardization for D-dimer assays, it is essential for clinicians to carefully consider the variations in assay performance and the specific test characteristics of their institution. CONCLUSIONS: Standardizing D-dimer assays and developing modified pretest probability models specifically for cancer patients, along with adjusted cut-off values for D-dimer testing, could significantly enhance the accuracy and effectiveness of VTE diagnosis in this population.

Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics.

BACKGROUND: The advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making to provide mineable tissue information. The present study sought to evaluate the diagnostic and predictive value of a multiparametric approach involving radiomics texture analysis, dual-energy CT-derived iodine concentration (DECT-IC), and diffusion-weighted MRI (DWI) in participants with histologically proven pancreatic cancer. METHODS: In this study, a total of 143 participants (63 years ± 13, 48 females) who underwent third-generation dual-source DECT and DWI between November 2014 and October 2022 were included. Among these, 83 received a final diagnosis of pancreatic cancer, 20 had pancreatitis, and 40 had no evidence of pancreatic pathologies. Data comparisons were performed using chi-square statistic tests, one-way ANOVA, or two-tailed Student's t-test. For the assessment of the association of texture features with overall survival, receiver operating characteristics analysis and Cox regression tests were used. RESULTS: Malignant pancreatic tissue differed significantly from normal or inflamed tissue regarding radiomics features (overall P < .001, respectively) and iodine uptake (overall P < .001, respectively). The performance for the distinction of malignant from normal or inflamed pancreatic tissue ranged between an AUC of ≥ 0.995 (95% CI, 0.955-1.0; P < .001) for radiomics features, ≥ 0.852 (95% CI, 0.767-0.914; P < .001) for DECT-IC, and ≥ 0.690 (95% CI, 0.587-0.780; P = .01) for DWI, respectively. During a follow-up of 14 ± 12 months (range, 10-44 months), the multiparametric approach showed a moderate prognostic power to predict all-cause mortality (c-index = 0.778 [95% CI, 0.697-0.864], P = .01). CONCLUSIONS: Our reported multiparametric approach allowed for accurate discrimination of pancreatic cancer and revealed great potential to provide independent prognostic information on all-cause mortality.

Comparative analysis of phenotypic and genotypic antibiotic susceptibility patterns in Mycobacterium avium complex.

OBJECTIVE: Phenotypic (Sensititre Myco, pDST) and genotypic drug susceptibility testing (GenoType NTM DR, gDST) in M. avium complex (MAC) have become available as standardized assays, but comparable data is needed. This study aimed to investigate the phenotypic and genotypic drug susceptibility patterns in MAC clinical isolates. METHODS: Overall, 98 isolates from 85 patients were included. pDST and gDST were performed on all isolates and results compared regarding specificity and sensitivity using pDST as a reference method. The impact of drug instability on pDST results was studied using a biological assay over 14 days. In addition, the evolution of antimicrobial resistance was investigated in sequential isolates of 13 patients. RESULTS: Macrolide resistance was rare, 1.2% (95% CI 0.7-7.3) of isolates in the base cohort. No aminoglycoside resistances were found, but 14.1% of the studied isolates (95% CI 7.8-23.8) showed intermediate susceptibility. The GenoType NTM DR identified two out of four macrolide-resistant isolates. Antibiotic stability was demonstrated to be poor in rifampicin, rifabutin, and doxycycylin. CONCLUSIONS: pDST results in NTM for unstable antibiotics must be interpreted with care. A combination of pDST and gDST will be useful for the guidance of antimicrobial therapy in MAC-disease.